



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase			  =			logging.get_logger(__name__)

__UpperCAmelCase			  =			torch.device('''cpu''')
def 						UpperCamelCase      (  )      ->	Tuple:
   UpperCamelCase       :    List[str]		      =      'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase       :    Tuple		      =      Image.open(requests.get(snake_case__			,	stream=snake_case__      ).raw      )
   return im
def 						UpperCamelCase      (  snake_case__			:       str      )      ->	int:
   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]      )

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]      )

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]      )

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]      )
def 						UpperCamelCase      (  snake_case__			:       List[Any]			,	snake_case__			:       Optional[int]			,	snake_case__			:       List[Any]      )      ->	str:
   UpperCamelCase       :    Optional[int]		      =      dct.pop(snake_case__      )
   UpperCamelCase       :    int		      =      val
def 						UpperCamelCase      (  snake_case__			:       Optional[int]      )      ->	Optional[Any]:
   UpperCamelCase       :    List[str]		      =      []
   for k in state_dict.keys():
      UpperCamelCase       :    Optional[Any]		      =      k
      if ".pwconv" in k:
         UpperCamelCase       :    Dict		      =      k_new.replace('.pwconv'			,	'.point_wise_conv'      )
      if ".dwconv" in k:
         UpperCamelCase       :    Optional[Any]		      =      k_new.replace('.dwconv'			,	'.depth_wise_conv'      )
      if ".Proj." in k:
         UpperCamelCase       :    Optional[Any]		      =      k_new.replace('.Proj.'			,	'.proj.'      )
      if "patch_embed" in k_new:
         UpperCamelCase       :    Optional[Any]		      =      k_new.replace('patch_embed'			,	'swiftformer.patch_embed.patch_embedding'      )
      if "network" in k_new:
         UpperCamelCase       :    Dict		      =      k_new.split('.'      )
         if ls[2].isdigit():
            UpperCamelCase       :    Union[str, Any]		      =      'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]      )
         else:
            UpperCamelCase       :    int		      =      k_new.replace('network'			,	'swiftformer.encoder.network'      )
      rename_keys.append((k, k_new)      )
   return rename_keys
@torch.no_grad()
def 						UpperCamelCase      (  snake_case__			:       List[Any]			,	snake_case__			:       List[Any]			,	snake_case__			:       str      )      ->	List[Any]:
   UpperCamelCase       :    Dict		      =      SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   UpperCamelCase       :    str		      =      1000
   UpperCamelCase       :    List[Any]		      =      'huggingface/label-files'
   UpperCamelCase       :    str		      =      'imagenet-1k-id2label.json'
   UpperCamelCase       :    List[Any]		      =      json.load(open(hf_hub_download(snake_case__			,	snake_case__			,	repo_type='dataset'      )			,	'r'      )      )
   UpperCamelCase       :    List[Any]		      =      {int(snake_case__      ): v for k, v in idalabel.items()}
   UpperCamelCase       :    List[Any]		      =      idalabel
   UpperCamelCase       :    Dict		      =      {v: k for k, v in idalabel.items()}

   # size of the architecture
   if swiftformer_name == "swiftformer_xs":
      UpperCamelCase       :    Any		      =      [3, 3, 6, 4]
      UpperCamelCase       :    Optional[Any]		      =      [48, 56, 112, 220]

   elif swiftformer_name == "swiftformer_s":
      UpperCamelCase       :    Optional[Any]		      =      [3, 3, 9, 6]
      UpperCamelCase       :    str		      =      [48, 64, 168, 224]

   elif swiftformer_name == "swiftformer_l1":
      UpperCamelCase       :    Optional[Any]		      =      [4, 3, 10, 5]
      UpperCamelCase       :    Union[str, Any]		      =      [48, 96, 192, 384]

   elif swiftformer_name == "swiftformer_l3":
      UpperCamelCase       :    List[Any]		      =      [4, 4, 12, 6]
      UpperCamelCase       :    List[str]		      =      [64, 128, 320, 512]

   # load state_dict of original model, remove and rename some keys
   if original_ckpt:
      if original_ckpt.startswith('https'      ):
         UpperCamelCase       :    Any		      =      torch.hub.load_state_dict_from_url(snake_case__			,	map_location='cpu'			,	check_hash=snake_case__      )
      else:
         UpperCamelCase       :    Optional[Any]		      =      torch.load(snake_case__			,	map_location='cpu'      )
   UpperCamelCase       :    int		      =      checkpoint

   UpperCamelCase       :    str		      =      create_rename_keys(snake_case__      )
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(snake_case__			,	snake_case__			,	snake_case__      )

   # load HuggingFace model
   UpperCamelCase       :    str		      =      SwiftFormerForImageClassification(snake_case__      ).eval()
   hf_model.load_state_dict(snake_case__      )

   # prepare test inputs
   UpperCamelCase       :    Union[str, Any]		      =      prepare_img()
   UpperCamelCase       :    Optional[int]		      =      ViTImageProcessor.from_pretrained('preprocessor_config'      )
   UpperCamelCase       :    int		      =      processor(images=snake_case__			,	return_tensors='pt'      )

   # compare outputs from both models
   UpperCamelCase       :    Tuple		      =      get_expected_output(snake_case__      )
   UpperCamelCase       :    Optional[Any]		      =      hf_model(inputs['pixel_values']      ).logits

   assert hf_logits.shape == torch.Size([1, 1000]      )
   assert torch.allclose(hf_logits[0, 0:5]			,	snake_case__			,	atol=1E-3      )

   Path(snake_case__      ).mkdir(exist_ok=snake_case__      )
   print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""      )
   hf_model.save_pretrained(snake_case__      )


if __name__ == "__main__":
    __UpperCAmelCase			  =			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--swiftformer_name''',
        default='''swiftformer_xs''',
        choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
        type=str,
        help='''Name of the SwiftFormer model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default='''./converted_outputs/''',
        type=str,
        help='''Path to the output PyTorch model directory.''',
    )
    parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

    __UpperCAmelCase			  =			parser.parse_args()
    convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)





"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''CLIPImageProcessor'''
			lowercase        =     ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	int=None      ,SCREAMING_SNAKE_CASE_  :	str=None      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  None
				if "feature_extractor" in kwargs:
					warnings.warn(
					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
					    ''' instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
					lowerCAmelCase												=  kwargs.pop('''feature_extractor'''			)

				lowerCAmelCase												=  image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
					raise ValueError('''You need to specify an `image_processor`.'''			)
				if tokenizer is None:
					raise ValueError('''You need to specify a `tokenizer`.'''			)

				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def __call__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	List[str]=None      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=None      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=None      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	List[Any]:





				"""simple docstring"""


				if text is None and images is None:
					raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if images is not None:
					lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if text is not None and images is not None:
					lowerCAmelCase												=  image_features.pixel_values
					return encoding
				elif text is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_			)      ,tensor_type=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Optional[Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,*SCREAMING_SNAKE_CASE_  :	int      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


